The potential use cases for generative AI to improve customer experiences are vast, ranging from the automation of processes like content development and data collection to applications in service and operations.

Nevertheless, marketers should not necessarily rush in, according to some experts at specialist agencies, customer service and SaaS businesses, who we spoke to about current trends in customer experience.

Julius Kontiola, lead strategist and head of experience strategy at Kallan & Co, a design studio that works with tech-focused businesses, told Econsultancy marketers should “treat AI investments like any other financial move.” 

“The real value in anything powered by AI isn’t in the tech itself,” he said, “but in how it kicks up quality or efficiency.”

“If you spot this potential in your business, go ahead and dive in. And if you can’t quite pin down an AI project that clicks right now keep an eye on the trend and funnel your investments into what really brings value.” he added. 

The real value… isn’t in the tech itself, but in how it kicks up quality or efficiency.

 

Julius Kontiola, Kallan & Co

Marketers seem to be experimenting with generative AI, perhaps in order to ensure they are able to take full advantage when the time is right. Econsultancy’s Future of Marketing survey found that 32% of marketers say their organisation is already using generative AI tools and 43% are actively considering doing so.

However, the industry is often rightly wary of being distracted by ‘shiny new things’, which in the past few years may have included technology such as augmented reality, the metaverse or NFTs. This shrewdness should be even more evident in the current environment of fragile consumer confidence, despite the obvious potential of generative AI.

“AI investments should be focused on enhancing human performance”

Indeed, the majority of experts that spoke to us about AI did so in somewhat cautionary terms, alluding to the danger of relying on AI as a silver bullet for CX.

AI isn’t a replacement for the human element,” said Mark Zablan, CEO of Emplifi.

“After all, AI models are only as accurate as the data they’re built on…” continued Zablan. “Instead, AI investments should be focused on enhancing human performance – helping CX teams to track customer behaviours, analyse performance and carry out a host of other activities that would otherwise be manual, time-consuming and cumbersome.”

Looking at responses to Econsultancy’s Future of Marketing survey (figure 1), those manual and time-consuming jobs feature highly as use cases for AI. Over three quarters are considering or actively using the technology to summarise emails and meetings, for example, and a whopping 90% are using or considering GenAI for content creation or copywriting.

“Those who learn to harness the power of AI technologies to enhance human talent will be those who succeed in the long term,” says Emplifi’s Mark Zablan .

Figure 1: What use cases for generative AI is your organisation already either pursuing or considering?

AI most successful when ‘collaborative with humans’

Jonathan Rosenberg, CTO and Head of AI at Five9, a contact centre as a service (CCaaS) provider, highlighted intelligent virtual assistants (IVAs) saying that AI’s most successful use cases are those that are collaborative with humans.”

“For example, Intelligent virtual assistants (IVAs) can resolve any routine order enquiries and triage more complex issues such as lost packages to human agents. In taking on administrative tasks, AI can take the weight off human agents and make it easier for businesses to avoid employee burnout and maximise resourcing,” he said. 

Customer service and generative AI: a uniquely transformational moment for the industry

This is a commonly cited use cases, but Rosenberg also highlighted the power of AI to spot patterns in call data and understand customer’s relationship with a brand, calling AI a “great historian”.

“It can collect call data to help human agents better understand the reasons why a customer might hang up or become frustrated,” he said. “Aggregating a clear picture of recurring issues means areas for improvement can be easily pinpointed, while the customer receives the level of empathy from a human agent that they deserve. It’s about combining the speed and scale of AI with human emotion – not using it in isolation.”

One important project is looking at how we can use Artificial Intelligence to help our teams communicate more clearly with customers, in a way that more accurately addresses the nature of each individual enquiry.

 

Next, half-year results, July 2023

There are signs of more and more businesses investing here. Next’s recent half year results mentioned AI in its section on customer contact improvements:

“The project to improve our call handling and customer service systems is by no means complete. We expect to deliver further improvements as the year progresses. One important project is looking at how we can use Artificial Intelligence to help our teams communicate more clearly with customers, in a way that more accurately addresses the nature of each individual enquiry. It feels like we are only scratching the surface of what might be achieved.”

“Easy access to human agents must remain a priority for CX teams”

Once again highlighting the need for caution, Prelini Udayan-Chiechi, SVP Global Marketing, Demand & Growth at Zendesk, told Econsultancy that companies should keep in mind that not all customers want AI as an end point in customer support.

“Our recent research suggests 81% of customers want AI to automatically route them to a human, based on how they’re feeling,” she said. “As we see businesses begin to integrate AI in CX, it’s important they maintain the human touch that customers still need. Customers are still adapting to AI and learning to trust its capabilities, that’s why easy access to human agents must remain a priority for CX teams. Connecting with human agents also builds stronger relationships with customers and can drive brand loyalty in the long run.” 

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Explore Econsultancy’s UX and CX training – including Understanding the Experience with Data, Mapping Customer Journeys, and Defining Customer Needs.

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